Network residing method and device, terminal and computer readable storage medium

Abstract

The invention relates to a network stationing method and device, a terminal and a computer readable storage medium. The method comprises the following steps: acquiring a target resident frequency point of a terminal in a preset frequency band according to frequency sweeping information of the preset frequency band and a first network model; the preset frequency band is a frequency band scanned by the terminal during frequency sweeping in a historical time period; according to the target residence frequency point and feature information of a candidate cell corresponding to the target residence frequency point, acquiring a residence success rate of the candidate cell under the target residence frequency point; and determining a target cell from the candidate cells according to the residence success rate. By adopting the method, the network residing efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device, terminal and computer-readable storage medium for on-line communication. Background Art

[0002] When a terminal accesses a mobile communication network such as 4G or 5G for initial network stationing, it is necessary to perform cell scanning and cell selection so that the terminal can reside in the mobile communication network.

[0003] In the traditional technology, cell scanning and cell selection are mainly performed based on the provisions of the communication protocol. However, the traditional method has the problem of low network efficiency. Summary of the invention

[0004] The embodiments of the present application provide a method, device, terminal and computer-readable storage medium for on-line operation, which can improve on-line operation efficiency.

[0005] In a first aspect, an embodiment of the present application provides a method for staying on a network, the method comprising:

[0006] According to the frequency scanning information of the preset frequency band and the first network model, a target resident frequency point of the terminal in the preset frequency band is acquired; the preset frequency band is a frequency band scanned by the terminal when performing frequency scanning in a historical time period;

[0007] According to the target resident frequency point and characteristic information of the candidate cell corresponding to the target resident frequency point, obtaining a resident success rate of the candidate cell at the target resident frequency point;

[0008] A target cell is determined from the candidate cells according to the residency success rate.

[0009] In a second aspect, an embodiment of the present application provides a network-based device, the device comprising:

[0010] A first acquisition module is used to acquire a target resident frequency point of the terminal in the preset frequency band according to the frequency scanning information of the preset frequency band and the first network model; the preset frequency band is a frequency band scanned by the terminal when performing frequency scanning in a historical time period;

[0011] A second acquisition module is used to acquire a residence success rate of the candidate cell at the target residence frequency point according to the target residence frequency point and characteristic information of the candidate cell corresponding to the target residence frequency point;

[0012] The first determination module is used to determine a target cell from the candidate cells according to the residency success rate.

[0013] In a third aspect, an embodiment of the present application provides a terminal, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the on-line method described in the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0015] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements the steps of the method described in the first aspect when executed by a processor.

[0016] The above-mentioned network stationing method, device, terminal and computer-readable storage medium can obtain the target station frequency point of the terminal in the preset frequency band according to the scanning information of the preset frequency band scanned by the terminal when scanning in the historical time period and the first network model. Compared with the terminal scanning all frequency bands supported by the mobile network at the location, the search range of the target station frequency point is narrowed and the scanning time of the terminal is reduced, so that the terminal can quickly obtain the station success rate of the candidate cell at the target station frequency point according to the characteristic information of the candidate cell corresponding to the target station frequency point, thereby improving The efficiency of the terminal in acquiring the residence success rate of the candidate cells at the target residence frequency point is improved. Further, the terminal can quickly determine the target cell from the candidate cells according to the residence success rate of the candidate cells at the target residence frequency point, thereby improving the network efficiency of the terminal. In addition, since the terminal obtains the target residence frequency point of the terminal in the preset frequency band based on the scanning information of the preset frequency band scanned during the scanning in the historical time period and the first network model, the terminal uses the existing scanning experience of the terminal to reduce the scanning time of the terminal while ensuring the accuracy of the acquired target residence frequency point, thereby improving the network success rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A diagram of an application environment of a method for stationing on a network in an embodiment;

[0019] Figure 2 A flowchart of a method for staying on the network in one embodiment;

[0020] Figure 3 A flowchart of a method for staying on the network in another embodiment;

[0021] Figure 4 A flowchart of a method for staying on the network in another embodiment;

[0022] Figure 5 A flowchart of a method for staying on the network in another embodiment;

[0023] Figure 6 is a structural block diagram of a network-based device in an embodiment;

[0024] Figure 7 FIG. 1 is a schematic diagram of the internal structure of a terminal in an embodiment. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] The network access method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 relies on the base station 104 as a bridge to access the core network. The base station 104 is a device deployed in the access network to provide wireless communication functions for the terminal. The base station 104 can manage one or more cells. In systems using different wireless access technologies, the names of devices with base station functions may be different. For example, in the LTE system, it is called an evolved node (evolved Node B, eNodeB) or eNB; in the 5G new air interface (New Radio, NR) system, it is called gNodeB or gNB. The base station 104 in the embodiment of the present application can be any type of base station device such as a macro base station, a micro base station or a pico base station. The terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets and portable wearable devices. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc.

[0027] In one embodiment, Figure 2 As shown, a method for stationing a network is provided, and the method is applied to Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0028] S201, acquiring a target resident frequency point of a terminal in a preset frequency band according to frequency scanning information of a preset frequency band and a first network model; the preset frequency band is a frequency band scanned by the terminal when performing frequency scanning in a historical time period.

[0029] Among them, the preset frequency band is the frequency band scanned by the terminal when performing frequency scanning in the historical time period. For example, it can be the frequency band scanned by the terminal when performing frequency scanning in the previous 30 days. In addition, the preset frequency band can be continuously updated and expanded based on the neighboring frequency band information obtained after the terminal successfully scans the frequency. Exemplarily, for a 5G network, the frequency scanning information of the preset frequency band may include the global synchronization channel number, the frequency scanning energy value, the access success rate, the number of frequency scanning times, the frequency scanning time, etc. For a 4G network, the frequency scanning information of the preset frequency band may include the main synchronization signal, the auxiliary synchronization signal, the frequency scanning energy value, the access success rate, the number of frequency scanning times, the frequency scanning time, etc. As an optional implementation method, in addition to the above-mentioned selected parameters, the frequency scanning information can also add the public land mobile network (PLMN) information where the terminal has resided, and the frequency band information of the neighboring area obtained after the frequency scanning resides successfully.

[0030] The target resident frequency point of the terminal in the preset frequency band obtained in this embodiment may be the optimal resident frequency point of the terminal in the preset frequency band. In this embodiment, data cleaning processing may also be performed on the sweep frequency information of the preset frequency band to ensure the quality of the sweep frequency information of the preset frequency band by removing invalid data, filling missing values, and correcting erroneous records, and then the target resident frequency point is obtained based on the sweep frequency information after data cleaning processing and the first network model.

[0031] In this embodiment, the current location of the terminal may be determined first, and then the frequency band scanned by the terminal when performing frequency scanning at the location in the historical time period may be determined as the preset frequency band; or the terminal may directly determine all frequency bands scanned when performing frequency scanning in the historical time period as the preset frequency band. Optionally, in this embodiment, the terminal may perform frequency scanning in the historical time period based on the provisions in the communication protocol, for example, the frequency scanning may be performed according to a channel grid at a certain interval.

[0032] In this embodiment, the first network model can be constructed based on a neural network, and its structure may include an input layer, multiple intermediate layers and an output layer, and the output layer can output the optimal resident frequency point under the input frequency band. In the model training stage, the scanning frequency information of the terminal in the scanning frequency band under different wireless environments can be collected, such as the scanning frequency information described in the above embodiment, to train the first network model. Alternatively, as an optional implementation, the first network model can be a model constructed based on the combination of a neural network and reinforcement learning, the neural network performs parameter prediction, and reinforcement learning continuously completes the model parameters according to the penalty function. In addition, a simple sigmoid function can be used for the selection of the neural network activation function, and a tanh function, a softmax function, and a PReLU activation function can also be used to improve the convergence speed and prediction accuracy. In addition, the model structure of the first network model can also be expanded, and the number of layers of the model can be increased to improve the prediction accuracy. It should also be noted that, for the deployment of the first network model, although offline deployment can be localized and is sufficient for the current model with a smaller parameter set, the training volume may be insufficient for the expanded model, and the first network model needs to be deployed to the cloud. However, there are information security issues in the cloud deployment of the first network model. A distributed deployment method can be adopted, for example, similar to the Linux operating system, where each terminal can be used as a distributed deployment client or as a server for training and processing.

[0033] S202, acquiring a residence success rate of the candidate cell at the target residence frequency point according to the target residence frequency point and characteristic information of the candidate cell corresponding to the target residence frequency point.

[0034] Usually, one frequency point can correspond to one cell or multiple cells. In this embodiment, the candidate cell corresponding to the target resident frequency point can be first determined, and then, according to the target resident frequency point and the characteristic information of the candidate cell corresponding to the target resident frequency point, the resident success rate of the candidate cell at the target resident frequency point is obtained. Optionally, in this embodiment, the received signal strength indication of the candidate cell at the target resident frequency point can be determined according to the characteristic information of the target resident frequency point and the candidate cell, and the resident success rate of the candidate cell at the target resident frequency point can be obtained according to the received signal strength indication of the candidate cell. Optionally, the characteristic information of the candidate cell in this embodiment may include at least one of the identification information of the candidate cell, the historical decoding success rate, the S value, the bandwidth, the reference signal received power, the signal to interference plus noise ratio, and the historical resident success rate. Optionally, the above-mentioned characteristic information may also include the neighboring cells, signals, and service quality information of the historical resident service cells, layer 2 and layer 1 indicator information, etc. obtained after the frequency sweeping and resident is successful. Optionally, the reference signal received power and signal to interference plus noise ratio in this embodiment may include the reference signal received power and signal to interference plus noise ratio of the main set antennas, and the reference signal received power and signal to interference plus noise ratio of the main set multiple-input multiple-output antennas.

[0035] S203: Determine a target cell from the candidate cells according to the residency success rate.

[0036] Optionally, in this embodiment, the candidate cells are sorted according to the residence success rate of the candidate cells at the target residence frequency, and then the candidate cells with stronger signal strength and higher historical residence success rate among the top-ranked candidate cells are determined as target cells. For example, if the candidate cells include four cells A, B, C, and D, the order of the candidate cells after sorting according to the residence success rate of the candidate cells at the target residence frequency is B, A, D, and C, and the signal strength of candidate cell A is stronger and the historical residence success rate is higher, then the candidate cell can be determined as the target cell, or, if the signal strength of candidate cell A is weaker but the historical residence success rate is higher, and the signal strength of candidate cell B is stronger but the historical residence success rate is lower than that of candidate cell A, then in this case, candidate cell B can also be determined as the target cell.

[0037] In the above-mentioned network resident method, according to the scanning information of the preset frequency band scanned by the terminal when performing frequency scanning in the historical time period and the first network model, the target resident frequency point of the terminal in the preset frequency band can be obtained. Compared with the terminal scanning all frequency bands supported by the mobile network at the location, the search range of the target resident frequency point is narrowed, and the scanning time of the terminal is reduced, so that the terminal can quickly obtain the resident success rate of the candidate cell at the target resident frequency point according to the target resident frequency point and the characteristic information of the candidate cell corresponding to the target resident frequency point, thereby improving the terminal to obtain the candidate cell. The efficiency of the cell's success rate of residence at the target residence frequency point can be further improved, and the terminal can quickly determine the target cell from the candidate cells according to the success rate of residence of the candidate cells at the target residence frequency point, thereby improving the terminal's network efficiency; in addition, since the terminal obtains the target residence frequency point of the terminal in the preset frequency band based on the scanning information of the preset frequency band scanned during the historical time period and the first network model, by using the terminal's existing scanning experience, it is possible to reduce the terminal's scanning time while ensuring the accuracy of the acquired target residence frequency point, thereby improving the network success rate.

[0038] In some scenarios, in order to improve the efficiency of obtaining the residence success rate of the candidate cell at the target residence frequency, it can be obtained through a neural network model. In this embodiment, the detailed process of obtaining the residence success rate of the candidate cell at the target residence frequency is explained. In one embodiment, the above S202 includes: inputting the target residence frequency and feature information into the second network model to obtain the residence success rate.

[0039] In this embodiment, the characteristic information of the candidate cell corresponding to the target resident frequency point can first be cleaned to remove invalid data, fill missing values ​​and correct erroneous records to ensure the data quality of the characteristic information of the candidate cell and obtain the processed characteristic information. Then, the target resident frequency point and the processed characteristic information are input into the second network model to obtain the residence success rate of the candidate cell at the target resident frequency point.

[0040] Similarly, the second network model in this embodiment can be constructed based on a neural network, and its structure may include an input layer, multiple intermediate layers and an output layer, and the output layer can output the residence success rate of the candidate cell at the target residence frequency. Alternatively, as an optional implementation, the second network model can be a model constructed based on the combination of neural networks and reinforcement learning, the neural network performs parameter prediction, and reinforcement learning continuously completes the model parameters according to the penalty function. In addition, a simple sigmoid function can be used for the selection of the neural network activation function, and a tanh function, a softmax function, and a PReLU activation function can also be used to improve the convergence speed and prediction accuracy. In addition, the model structure of the second network model can also be expanded, and the number of layers of the model can be increased to improve the prediction accuracy. It should also be noted that for the deployment of the second network model, although offline deployment can be localized, it is sufficient for the current model with a smaller parameter set, but the training volume may be insufficient for the expanded model, and the second network model needs to be deployed to the cloud, but the cloud deployment of the second network model has information security and other issues, and can be carried out in a distributed manner, such as similar to the Linux operating system, each terminal can be used as a distributed deployment client or as a server for training and processing.

[0041] In this embodiment, the characteristic information of the target residence frequency and the candidate cell corresponding to the target residence frequency is input into the second network model, and the second network model can be used to quickly obtain the residence success rate of the candidate cell at the target residence frequency, thereby improving the efficiency of obtaining the residence success rate of the candidate cell at the target residence frequency.

[0042] In the scenario of determining the target cell, the candidate cell can be decoded, the S value of the candidate cell can be calculated, and the target cell can be determined according to the S value of the candidate cell. In this embodiment, the specific process of determining the target cell is explained. In one embodiment, Figure 3 As shown, the above S203 includes:

[0043] S301, sorting candidate cells according to the residency success rate, and determining the decoding order of the candidate cells.

[0044] In this embodiment, the candidate cells may be sorted in descending order according to the residency success rate of the candidate cells to determine the decoding order of the candidate cells. Alternatively, as another optional implementation, the candidate cells with a residency success rate higher than a threshold value of 90% may be determined, and then the candidate cells higher than the threshold value may be placed in front to determine the decoding order of the candidate cells, so that the candidate cells with a high residency success rate may be decoded first.

[0045] S302, decoding the candidate cells according to the decoding order, and determining the candidate cells satisfying the S criterion as the target cells.

[0046] In this embodiment, the candidate cells may be decoded according to the determined decoding order, the master information block and the system information block of each candidate cell may be decoded in turn, and then the S value of each candidate cell may be calculated according to the S criterion specified in the communication protocol, and the candidate cell whose S value meets the S criterion may be determined as the target cell. Optionally, in this embodiment, if the decoding of the candidate cells fails according to the above decoding order, the candidate cells may be sorted according to their energy values, the master information block and the system information block of each candidate cell may be decoded in turn, and then the S value may be calculated according to the S criterion, and the candidate cell that meets the S value may be determined as the target cell.

[0047] In addition, it should be noted that if a candidate cell that meets the S criterion is not determined from the candidate cells according to the above method, it may mean that the acquired target residence frequency is not accurate enough, and it is necessary to re-scan the cell to determine a new target residence frequency, and determine the target cell based on the new target residence frequency. As an optional implementation method, the terminal can perform cell scanning based on the provisions of the communication protocol to re-determine the target cell.

[0048] In this embodiment, the candidate cells are sorted according to their residence success rates at the target residence frequency points, so that the candidate cells with high residence success rates can be ranked in front, so that the candidate cells with high residence success rates can be decoded according to the decoding order of the candidate cells, thereby improving the efficiency of determining the target cell and saving the power consumption of the terminal.

[0049] In some scenarios, cell reselection may be required after the target cell is determined. Figure 4 As shown, the above method also includes:

[0050] S401, obtaining cell reselection information related to a target cell.

[0051] In this embodiment, after the target cell is determined, cell reselection information related to the target cell can also be obtained based on the location information of the terminal and the historical cell reselection information of the terminal. For example, the cell reselection information may include the identification, priority, residence success rate, bandwidth, decoding success rate and other information of the reselected cell in the neighboring cells of the target cell.

[0052] S402: Input the cell reselection information into a preset third network model to obtain a reselection success rate from a target cell to a neighboring cell of the target cell.

[0053] The third network model in this embodiment can be constructed based on a neural network, and its structure may include an input layer, multiple intermediate layers and an output layer, and the output layer can output the reselection success rate of the target cell to the neighboring cell of the target cell. Alternatively, as an optional implementation, the third network model can be a model constructed based on the combination of a neural network and reinforcement learning, the neural network performs parameter prediction, and the reinforcement learning continuously completes the model parameters according to the penalty function. In this embodiment, the cell reselection information can be used as a training sample in advance to train the third network model, so that the third network model can output the reselection success rate from the target cell to the neighboring cell of the target cell.

[0054] S403: Determine a reselected cell from adjacent cells according to the reselection success rate.

[0055] It is understandable that the higher the reselection success rate of the target cell to the neighboring cell of the target cell, the higher the probability of the cell being reselected. In this embodiment, the neighboring cells of the target cell can be sorted in descending order according to the reselection success rate, and the neighboring cell with the highest reselection success rate is determined as the reselected cell.

[0056] In this embodiment, the cell reselection information related to the target cell can be obtained through the historical cell reselection information of the terminal, so that the cell reselection information related to the target cell can be input into the preset third network model, and the reselection success rate of the neighboring cell from the target cell to the target cell can be quickly obtained through the third network model, and then the reselected cell can be quickly determined from the neighboring cells according to the reselection success rate of the neighboring cells of the target cell, saving time and power consumption of the terminal when performing cell reselection.

[0057] In some scenarios, in the connected state, the terminal may also release the network connection according to the instruction of the current network redirection command, perform cell redirection, and then re-initiate a service request to restore the original service. Figure 5 As shown, the above method also includes:

[0058] S501: Acquire a resident frequency point related to a target cell.

[0059] In this embodiment, the resident frequency associated with the target cell may be the above target resident frequency or other resident frequency. Optionally, in this embodiment, the terminal may determine the resident frequency associated with the target cell based on historical cell redirection information.

[0060] S502: Input the resident frequency points related to the target cell into a preset fourth network model to obtain a redirection success rate from the target cell to a neighboring cell of the target cell.

[0061] The fourth network model in this embodiment can be constructed based on a neural network, and its structure may include an input layer, multiple intermediate layers and an output layer, and the output layer can output the redirection success rate of the target cell to the neighboring cell of the target cell. Alternatively, as an optional implementation, the third network model can be a model constructed based on the combination of a neural network and reinforcement learning, the neural network performs parameter prediction, and reinforcement learning continuously completes the model parameters according to the penalty function. In this embodiment, the fourth network model can be trained in advance according to the resident frequency points related to the target cell, so that the redirection success rate from the target cell to the neighboring cell of the target cell can be output through the fourth network model.

[0062] S503: Determine a redirection cell from neighboring cells according to the redirection success rate.

[0063] It is understandable that the higher the redirection success rate of the target cell to the neighboring cell of the target cell, the higher the probability that the cell is redirected. In this embodiment, the neighboring cells of the target cell can be sorted in descending order according to the redirection success rate, and the neighboring cell with the highest redirection success rate is determined as the redirected cell.

[0064] In this embodiment, the resident frequency points related to the target cell can be obtained through the historical cell redirection information of the terminal, so that the resident frequency points related to the target cell can be input into the preset fourth network model, and the redirection success rate from the target cell to the adjacent cell of the target cell can be quickly obtained through the fourth network model, and then the redirection cell can be quickly determined from the adjacent cells according to the redirection success rate of the adjacent cells of the target cell, saving time and power consumption of the terminal when performing cell redirection.

[0065] In order to facilitate the understanding of those skilled in the art, the network access method provided by the present application is described in detail below in conjunction with a complete embodiment:

[0066] S1, according to the scanning information of the preset frequency band and the first network model, obtaining the target resident frequency point of the terminal in the preset frequency band; the preset frequency band is the frequency band scanned by the terminal when performing frequency scanning in a historical time period.

[0067] S2, inputting characteristic information of the target resident frequency point and the candidate cells corresponding to the target resident frequency point into a second network model, and obtaining a resident success rate of the candidate cells at the target resident frequency point.

[0068] S3, sorting the candidate cells according to the residency success rate, and determining the decoding order of the candidate cells.

[0069] S4, decoding the candidate cells according to the decoding order, and determining the candidate cells that meet the S criterion as the target cells.

[0070] S5: If a candidate cell satisfying the S criterion is not determined from the candidate cells, a cell scan is performed based on the provisions of the communication protocol to determine a target cell.

[0071] S6, obtaining cell reselection information related to the target cell.

[0072] S7, inputting the cell reselection information into a preset third network model, and obtaining a reselection success rate from the target cell to a neighboring cell of the target cell.

[0073] S8. Determine a reselected cell from adjacent cells according to the reselection success rate.

[0074] S9, obtaining the resident frequency point related to the target cell.

[0075] S10: Input the resident frequency points related to the target cell into a preset fourth network model to obtain a redirection success rate from the target cell to a neighboring cell of the target cell.

[0076] S11, determining a redirection cell from neighboring cells according to the redirection success rate.

[0077] It should be noted that for the descriptions in the above S1-S11, reference may be made to the relevant descriptions in the above embodiments, and the effects are similar, so this embodiment will not be repeated here.

[0078] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0079] Based on the same inventive concept, the embodiment of the present application also provides a network-based device for implementing the network-based method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more network-based device embodiments provided below can refer to the limitations of the network-based method above, and will not be repeated here.

[0080] In one embodiment, Figure 6As shown, a network-based device is provided, including: a first acquisition module, a second acquisition module and a first determination module, wherein:

[0081] The first acquisition module is used to acquire a target resident frequency point of the terminal in the preset frequency band according to the scanning information of the preset frequency band and the first network model; the preset frequency band is the frequency band scanned by the terminal when performing frequency scanning in a historical time period.

[0082] The second acquisition module is used to acquire the residence success rate of the candidate cell at the target residence frequency point according to the target residence frequency point and the characteristic information of the candidate cell corresponding to the target residence frequency point.

[0083] The first determination module is used to determine a target cell from candidate cells according to a residency success rate.

[0084] Optionally, the above-mentioned characteristic information includes at least one of the identification information of the candidate cell, historical decoding success rate, S value, bandwidth, reference signal receiving power, signal to interference plus noise ratio, and historical residence success rate.

[0085] The on-network device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0086] Based on the above embodiment, optionally, the above second acquisition module includes: an acquisition unit, wherein:

[0087] The acquisition unit is used to input the target resident frequency point and characteristic information into the second network model to obtain the resident success rate.

[0088] The on-network device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0089] On the basis of the above embodiment, optionally, the above first determination module includes: a first determination unit and a second determination unit, wherein:

[0090] The first determining unit is used to sort the candidate cells according to the residency success rate and determine the decoding order of the candidate cells.

[0091] The second determining unit is configured to decode the candidate cells according to the decoding order, and determine the candidate cells satisfying the S criterion as the target cells.

[0092] The on-network device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0093] Based on the above embodiment, optionally, the above device further includes: a second determining module, wherein:

[0094] The second determination module is used to perform cell scanning based on the provisions of the communication protocol to determine the target cell if a candidate cell that meets the S criterion is not determined from the candidate cells.

[0095] The on-network device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0096] On the basis of the above embodiment, optionally, the above device further includes: a third acquisition module, a fourth acquisition module and a third determination module, wherein:

[0097] The third acquisition module is used to acquire cell reselection information related to the target cell.

[0098] The fourth acquisition module is used to input the cell reselection information into a preset third network model to obtain a reselection success rate from the target cell to the neighboring cell of the target cell.

[0099] The third determination module is used to determine the reselected cell from the adjacent cells according to the reselection success rate.

[0100] The on-network device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0101] On the basis of the above embodiment, optionally, the above device further includes: a fifth acquisition module, a sixth acquisition module and a fourth determination module, wherein:

[0102] The fifth acquisition module is used to acquire the resident frequency point related to the target cell.

[0103] The sixth acquisition module is used to input the resident frequency point related to the target cell into the preset fourth network model to obtain the redirection success rate from the target cell to the neighboring cell of the target cell.

[0104] The fourth determining module is used to determine the redirected cell from the neighboring cells according to the redirection success rate.

[0105] The on-network device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0106] Each module in the above-mentioned network device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0107] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method of staying on the network is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0108] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0109] In one embodiment, a terminal is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0110] According to the frequency scanning information of the preset frequency band and the first network model, a target resident frequency point of the terminal in the preset frequency band is obtained; the preset frequency band is a frequency band scanned by the terminal when performing frequency scanning in a historical time period;

[0111] According to the target dwelling frequency point and the characteristic information of the candidate cell corresponding to the target dwelling frequency point, the dwelling success rate of the candidate cell at the target dwelling frequency point is obtained;

[0112] According to the residency success rate, the target cell is determined from the candidate cells.

[0113] Optionally, the above-mentioned characteristic information includes at least one of the identification information of the candidate cell, historical decoding success rate, S value, bandwidth, reference signal receiving power, signal to interference plus noise ratio, and historical residence success rate.

[0114] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0115] The target residence frequency and characteristic information are input into the second network model to obtain the residence success rate.

[0116] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0117] Sort the candidate cells according to the residency success rate and determine the decoding order of the candidate cells;

[0118] The candidate cells are decoded according to the decoding order, and the candidate cells satisfying the S criterion are determined as the target cells.

[0119] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0120] If a candidate cell satisfying the S criterion is not determined from the candidate cells, a cell scan is performed based on the provisions of the communication protocol to determine the target cell.

[0121] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0122] Obtaining cell reselection information related to the target cell;

[0123] Inputting the cell reselection information into a preset third network model to obtain a reselection success rate from the target cell to a neighboring cell of the target cell;

[0124] According to the reselection success rate, the reselected cell is determined from the adjacent cells.

[0125] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0126] Obtain the resident frequency points related to the target cell;

[0127] Inputting the resident frequency points related to the target cell into a preset fourth network model, and obtaining a redirection success rate from the target cell to a neighboring cell of the target cell;

[0128] According to the redirection success rate, a redirection cell is determined from the neighboring cells.

[0129] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0130] According to the frequency scanning information of the preset frequency band and the first network model, a target resident frequency point of the terminal in the preset frequency band is obtained; the preset frequency band is a frequency band scanned by the terminal when performing frequency scanning in a historical time period;

[0131] According to the target dwelling frequency point and the characteristic information of the candidate cell corresponding to the target dwelling frequency point, the dwelling success rate of the candidate cell at the target dwelling frequency point is obtained;

[0132] According to the residency success rate, the target cell is determined from the candidate cells.

[0133] Optionally, the above-mentioned characteristic information includes at least one of the identification information of the candidate cell, historical decoding success rate, S value, bandwidth, reference signal receiving power, signal to interference plus noise ratio, and historical residence success rate.

[0134] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0135] The target residence frequency and characteristic information are input into the second network model to obtain the residence success rate.

[0136] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0137] Sort the candidate cells according to the residency success rate and determine the decoding order of the candidate cells;

[0138] The candidate cells are decoded according to the decoding order, and the candidate cells satisfying the S criterion are determined as the target cells.

[0139] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0140] If a candidate cell satisfying the S criterion is not determined from the candidate cells, a cell scan is performed based on the provisions of the communication protocol to determine the target cell.

[0141] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0142] Obtaining cell reselection information related to the target cell;

[0143] Inputting the cell reselection information into a preset third network model to obtain a reselection success rate from the target cell to a neighboring cell of the target cell;

[0144] According to the reselection success rate, the reselected cell is determined from the adjacent cells.

[0145] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0146] Obtain the resident frequency points related to the target cell;

[0147] Inputting the resident frequency points related to the target cell into a preset fourth network model, and obtaining a redirection success rate from the target cell to a neighboring cell of the target cell;

[0148] According to the redirection success rate, a redirection cell is determined from the neighboring cells.

[0149] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0150] According to the frequency scanning information of the preset frequency band and the first network model, a target resident frequency point of the terminal in the preset frequency band is obtained; the preset frequency band is a frequency band scanned by the terminal when performing frequency scanning in a historical time period;

[0151] According to the target dwelling frequency point and the characteristic information of the candidate cell corresponding to the target dwelling frequency point, the dwelling success rate of the candidate cell at the target dwelling frequency point is obtained;

[0152] According to the residency success rate, the target cell is determined from the candidate cells.

[0153] Optionally, the above-mentioned characteristic information includes at least one of the identification information of the candidate cell, historical decoding success rate, S value, bandwidth, reference signal receiving power, signal to interference plus noise ratio, and historical residence success rate.

[0154] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0155] The target residence frequency and characteristic information are input into the second network model to obtain the residence success rate.

[0156] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0157] Sort the candidate cells according to the residency success rate and determine the decoding order of the candidate cells;

[0158] The candidate cells are decoded according to the decoding order, and the candidate cells satisfying the S criterion are determined as the target cells.

[0159] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0160] If a candidate cell satisfying the S criterion is not determined from the candidate cells, a cell scan is performed based on the provisions of the communication protocol to determine the target cell.

[0161] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0162] Obtaining cell reselection information related to the target cell;

[0163] Inputting the cell reselection information into a preset third network model to obtain a reselection success rate from the target cell to a neighboring cell of the target cell;

[0164] According to the reselection success rate, the reselected cell is determined from the adjacent cells.

[0165] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0166] Obtain the resident frequency points related to the target cell;

[0167] Inputting the resident frequency points related to the target cell into a preset fourth network model, and obtaining a redirection success rate from the target cell to a neighboring cell of the target cell;

[0168] According to the redirection success rate, a redirection cell is determined from the neighboring cells.

[0169] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0170] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0171] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for stationing on a network, characterized in that: The method comprises: According to the frequency scanning information of the preset frequency band and the first network model, a target resident frequency point of the terminal in the preset frequency band is obtained; the preset frequency band is a frequency band scanned by the terminal when performing frequency scanning in a historical time period; According to the target resident frequency point and characteristic information of the candidate cell corresponding to the target resident frequency point, obtaining a resident success rate of the candidate cell at the target resident frequency point; A target cell is determined from the candidate cells according to the residency success rate.

2. The method according to claim 1, characterized in that The acquiring, according to the target resident frequency point and the characteristic information of the candidate cell corresponding to the target resident frequency point, a resident success rate of the candidate cell at the target resident frequency point comprises: The target resident frequency point and the characteristic information are input into a second network model to obtain the resident success rate.

3. The method according to claim 2, characterized in that The characteristic information includes at least one of the identification information of the candidate cell, historical decoding success rate, S value, bandwidth, reference signal received power, signal to interference plus noise ratio, and historical residence success rate.

4. The method according to any one of claims 1 to 3, characterized in that: The determining a target cell from the candidate cells according to the residency success rate includes: Sorting the candidate cells according to the residency success rate to determine a decoding order of the candidate cells; The candidate cells are decoded according to the decoding order, and the candidate cells satisfying the S criterion are determined as the target cells.

5. The method according to claim 4, characterized in that The method further comprises: If a candidate cell satisfying the S criterion is not determined from the candidate cells, a cell scan is performed based on the provisions of the communication protocol to determine the target cell.

6. The method according to claim 1, characterized in that The method further comprises: Acquiring cell reselection information related to the target cell; Inputting the cell reselection information into a preset third network model to obtain a reselection success rate from a target cell to a neighboring cell of the target cell; A reselected cell is determined from the neighboring cells according to the reselection success rate.

7. The method according to claim 1, characterized in that The method further comprises: Acquire a resident frequency point related to the target cell; Inputting the resident frequency point related to the target cell into a preset fourth network model, and obtaining a redirection success rate from the target cell to a neighboring cell of the target cell; A redirection cell is determined from the neighboring cells according to the redirection success rate.

8. A network station device, characterized in that: The device comprises: A first acquisition module is used to acquire a target resident frequency point of the terminal in the preset frequency band according to the frequency scanning information of the preset frequency band and the first network model; the preset frequency band is a frequency band scanned by the terminal when performing frequency scanning in a historical time period; A second acquisition module is used to acquire a residence success rate of the candidate cell at the target residence frequency point according to the target residence frequency point and characteristic information of the candidate cell corresponding to the target residence frequency point; The first determination module is used to determine a target cell from the candidate cells according to the residency success rate.

9. A terminal comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the computer program is executed by the processor, the processor is caused to perform the steps of the on-line method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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